OK, the state of information maturity—how well organizations monetize, manage and measure their information assets—may not be worth throwing a tantrum over (or “spitting the dummy” for those you Down Under). But when compared to the rigor and process and discipline with which other “balance sheet” assets are managed, measured and monetized, the attention paid to information assets pales.

Just imagine for a moment a manufacturer that decides not to sell some of its products, just leaving them in the warehouse. Or imagine a retailer with no inventory of what’s on its store shelves; an accountant who has no way to gauge the company’s financial position; or an HR executive with no process for measuring employee performance. Ridiculous right? Well that’s more or less the state of information monetization, management and measurement today.

Only in the past few years have we seen the welcome emergence of an executive role specifically for tending to information: the chief data officer (CDO). This is 60 or 70 years since the rise of the chief financial officer, 30 or so years since chief human resource officers started appearing, and about 20 years since chief risk/security officers started being anointed. Yes chief information officers (CIOs) have been in place for decades, but their purview has been tilted toward the monetization, management and measurement of technologies. (Behind their backs, CIOs are referred to as chief infrastructure officers.) Information’s role has been relegated to an input and an output–at worst a byproduct, and at best a resource. But not a true asset.

However, digital business and analytics in particular have rendered it ever-more vital to the organization, intensifying the need for effective enterprise information rigor. This should have elevated the role of information to another economic asset. Yet data and analytics leaders such as the CDO struggle to make the case and therefore struggle to improve the organization’s information maturity.

Workshops I have conducted around the world assisting information and business leaders self-assess and remedy their state of enterprise information capabilities offer a lens to view typical and unique information maturity challenges.

The broad concerns of these leaders were about leadership, priorities, resources and corporate cultures that forestall advances. These concerns emerge from the attendees’ acute awareness of their present information-related capabilities. The vast majority of these leaders described their respective organization as either:

“Aware” — aware of information availability issues but unable to make significant progress to overcome them, or

In both cases, information leaders are responding to the dictates of circumstances, which repeat themselves because leaders cannot or do not devote the time needed to change those circumstances. In both cases, information leaders claim they are confused, uncertain or frustrated in breaking free of inertia and habitual approaches. Typically, workshop participants feel more confident in their organizational structure and roles, and quite satisfied (if not overwhelmed) with the level of technology at their disposal. On the other hand, many admit they have not addressed information-related metrics or information life cycle issues at all.

The root cause of most issues seems to stem from information (even big data) being considered an infant compared to other grown-up enterprise assets. Like a child who cannot vote or drive, information is not considered an asset according to accounting standards, and not considered property by most insurance policies. As a result, information is not counted or quantified by most organizations, leading to immature information management practices and squandered monetization opportunities.

Additional Resources

Doug Laney
VP and Distinguished Analyst, Data & Analytics Strategy12 years at Gartner 30 years in IT industry

Doug Laney is a research vice president and distinguished analyst with Gartner. He advises clients on data and analytics strategy, information innovation, and infonomics (measuring, managing and monetizing information as an actual corporate asset). Follow Doug on Twitter @Doug_Laney...Read Full Bio

Thoughts on Information Maturity (Waaaaaaah!!!)

Absolutely agree with you, Doug, on a very timely topic. Cyber assets – and a related liability based on the organization’s comprehensive attack surface and measured data exposure – need to be added to the balance sheet. We are currently in the process of articulating a method for calculating the liability complement to the asset considerations you have already described. I would love the opportunity to have a 1-hour discussion with you regarding this.

If Chief Data Officers follow their other “C” level brethren’s effectiveness, then data improvement is doomed. CTO’s and CIO’s have been ineffective in addressing the most fundamental best practices of their profession; project management and quality. Projects are continually late and over budget and typically don’t deliver the expected results. Chief Human Resource Officers are administrators of payroll, and benefits. However, managing human resources is more relevant to data management than technology and finance.

Humans are by nature subjective, unpredictable resources. Data, which is nothing more than a language created by humans is also subjective and unpredictable. Chief Data Officers typically come from a technical background and bring little in the way of skills dealing with data from a human perspective. They typically have few skills working with ontologies and taxonomies and know little about semantics or pragmatics. Thus, they resort to technology, like the CTO or CIO.

Data is a difficult resource to manage. Trying to monetize data is like trying to monetize employee intelligence. A fruitless effort even those some have tried.

Data is unfortunately designed by technologists lacking the same skills the CDO. Thus, current data modeling practices remain archaic and continue to contribute to data pollution. The most common problem with data is its quality. The most common data quality problem is poor metadata. The root cause of poor metadata is the lack of semantics and pragmatics that went into the design of the data.

CDO’s do nothing to address this problem because it requires attention to detail and continuous improvement.

Visualize a CDO standing up in front of the board of directors and stating they are going to design better data using principles of ontology, semantics and pragmatics. Versus he or she standing up and saying we are building a data lake (typically a swamp) or we are building a master data management solution because our data in the numerous silos is of poor quality but bringing it together will result in a golden copy. Talk about alchemy.

We have seen little change in the best practices regarding data design despite data governance and other such marketing lingo. You can’t measure the data maturity of an organization if they remain data illiterate. The primary role of a CDO should be to become data literate and to ensure the organization becomes data literate, including the other “C” level functions and the board. Only then will an organization be able to assess the value of its data based on the knowledge and skills about data.

Thanks for the comment Richard. Wow that’s a lot to unpack. Let’s give it a go:

1. No disagreement that other C-level leaders struggle to manage initiatives to on-time, on-budget completion that achieve advertised benefits. Perhaps “agile” methods should be applied at a strategic level, not just for app dev projects. Especially in today’s dynamic and rapidly digitalizing business environment with complex, shifting business ecosystems and continuous disintermediation, traditional modes of developing and executing on strategy are no longer relevant.
2. I think you’re short-changing the CHRO’s role. Don’t forget about recruiting, hiring, training, reviewing/rating, locating and relocating, and firing and downsizing as part of the human capital management exercise. You’ll see in the Infonomics book I’m publishing next summer how many management activities from other disciplines like HR can and should be applied in an information context.
3. I argue that since humans are not balance sheet assets (thankfully due to the 13th Amendment and other similar anti-slavery laws in other civilized countries), HR execs may not be the best example of managing an intangible proto-asset such as information. Those who manage other kinds of monetizable IP (e.g. patents, trademarks, copyrights) with similar non-rivalrous and non-depletable characteristics might offer the best examples of how to manage information as an asset.
4. Actually, you’ll be pleased to know that our research shows only 9% of CDOs come from the IT dept. See our latest research on the CDO role: “Survey Analysis: The Career Path to the Chief Data Officer Role” http://www.gartner.com/doc/3319717. But yes, most CDOs have a long way to go to deal with data from a human perspective. Still, let’s keep in mind that most “users” of information today are NOT human, they are applications or machines or devices. And this trend is accelerating.
5. Agreed data is difficult to manage, mostly because it’s too easily copied and distributed, too easily misunderstood, and too contextual.
6. I disagree that monetizing data “is like trying to monetize employee intelligence.” Employee intelligence is tacit and not codified as is information. At Gartner we have a library of 100s of examples of organizations monetizing data in a variety of ways. Some grocers are generating $100M in incremental revenue annually by licensing their data. It’s fairly easy, but most orgs lack the org structure, leadership or imperative to do so.
7. It’s not fair to say “data is designed by technologists.” Most data in fact is designed at a conceptual and logical level by business architects, but then is implemented physically by technologists. I’d agree that data modeling practices tend to be project-focused without much eye toward enterprise or future needs.
8. The lack of metadata ABOUT data quality is the biggest data quality problem. Few organizations measure their information assets’ accuracy, completeness, precision, scarcity, objectivity, etc. (Gartner has defined 12 DQ dimensions and how to measure each.) But accuracy and completeness are easily the granddaddies of data quality itself.
9. I’d argue that the root cause of poor metadata is simply a lack of focus/imperative/budget for capturing it. Something that should be on the CDO’s agenda.
10. CDOs should have team members who focus on the detail.
11. Correct, a CDO would not get up in front of a BOD and start spewing about ontology and semantics. They should be adept at putting these concepts into English (or their native language). My colleague Valerie Logan is about to publish some research on what she’s coined, “information as a second language (ISL)”.
12. The idea of a golden copy (or single version of the truth) has been proven to be a fantasy. “Customer” for example has a dozen different *legitimate* meanings in most organizations. Focus on a single source of truth or “golden source”, and reconciling ontological and contextual differences.
13. Isn’t data alchemy just another term for data monetization (which you discredited earlier)? But yes, CDOs should be talking to the board about the latent, unrealized value of information.
13. True, there are opportunities for an evolution (or even revolution) in data design. But suggesting data governance is marketing lingo, is a bit hyperbolic.
14. Not sure I understand why you can’t measure data maturity if the organization is data illiterate. That’s like suggesting you can’t measure the capabilities of a person who cannot read. If so, then your measurement instrument is the problem. The Gartner Information Maturity Model helps quantify and advise on organizational capabilities for any kind of organization. (See https://www.gartner.com/doc/3236418)
15. The *probable* value of information, yes, is based in part upon the knowledge and skills about data. But information’s value can be gauged in other ways: its intrinsic value, its business value, its market value, its cost, its contribution to revenue. These are the six information valuation models I published last year, which our clients are starting to deploy (mostly in combination) to make the kinds of business case you’re talking about. (See Why and How to Value Your Information as an Asset http://www.gartner.com/smarterwithgartner/why-and-how-to-value-your-information-as-an-asset/)

Doug, thanks for your informative response. Working in the “trenches” provides a different perspective than the perception of middle and upper management. The realities are typically quite different.

1. David Knuth wrote the book Literate Programming that summarizes the need to document code for non-technical people to read. Agile suggests that documentation is secondary or even not required. This results in the continuous attempt to “bridge the gap” between IT and the business. In my experiences, Agile is an attempt to avoid the “thinking” work required to build an architecture and strategy with the promise of “if we build it, they will come”. Many Agile projects minimize the involvement of all the participants in the organization because it is difficult to achieve consensus. The result is the continued building of silos of solutions that prevent interoperability at all levels. Agile reminds me of when we used to do skunk works. Working in isolation we can build a solution faster. But does it satisfy the varied needs of each function and department? I suggest this is the trade-off of using Agile.

2. I look forward to your book but most HR departments are administrators not strategic thinkers. They react to events rather than forecasting the future. In the case of software programmers, training in coding languages is considered sufficient but many programmer I meet lack problem solving skills. Similarly, data modelers remain data illiterate and build database solutions that are technology focused rather than designs to enhance human communications. I suggest that HR should examine the need to hire librarians for information management and sociologist to help management the social aspects of IT.

3. Humans are balance sheet entities. They are a cost and liability. I suggest that many of these white-collar folks will be “downsized” in the future, despite the hype about data. There are numerous “data pushers” in organizations who spend their days moving data from one environment to another e.g. spreadsheets. Like manufacturing jobs, these positions will be replaced by automation. Referring to the role of HR, they should be anticipating this potential downsizing and plan for it.

4. “most “users” of information today are NOT human, they are applications or machines or devices”. This is the root of the problem with data. Technologists design data for machine consumption and therein lies the fundamental problem with data. Unfit for human consumption. This results in unnecessary rework, confusion and reinventing the wheel. Data should be designed for human consumption first and machine processing second. But if you are correct, then my previous suggestion of job loss above is inevitable.

6. “Selling” data is different from monetizing data within an organization. There are many organizations whose sole purpose is to sell data such as Thomson Reuters or Bloomberg. What is the value of financial data in an organization and why haven’t CFO’s “monetized” this most critical data?

7. Conceptual modeling seldom considers the social factors that go into the design. Factors such as bias, behaviors and organization culture are ignored. We lack techniques to factor in these critical factors into a design, preferring to use a technically dominated approach. There have been some attempts to introduce these factors into design such a preparing a Business Motivation model but few data modelers of for that matter CIO’s or CTO’s appreciate these social factors.

8. Metadata quality should be focused on the semantics and pragmatics of data. How do humans interpret and understand the data. Accuracy and completeness are focused on the instance data, rather than the metadata.

9. If an organization does not have a data dictionary including semantics and pragmatics of their data, they remain data illiterate. But crating and maintain a comprehensive data dictionary is a significant undertaking. This is where the talents of a librarian versed in techniques to make data usable, find-able and interpret-able is key. A CDO’s first task should be to acquire this resource.

11.I suggest that information and data is becoming the “first” language. This requires skills and understanding of how people communicate rather than how machines communicate.

13. Data alchemy is an attempt to turn data from its primary use such as processing transactions to using the data for analytical purposes. Few data modelers consider data design for use across the varied spectrum of potential uses and thus much of the data that is crated suits a single purpose or context. Then we apply alchemy to turn transaction data into “gold”.

14. With most data maturity models, we are using the data management measures rather than how effectively the data improves communications. If information and data is the language we now use, how mature is the data in facilitating communications?

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